2. Waiter_Tips_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WAITER TIPS PREDICTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  total_bill   tip     sex smoker  day    time  size\n",
       "0      16.99  1.01  Female     No  Sun  Dinner   2.0\n",
       "1      10.34  1.66    Male     No  Sun  Dinner   3.0\n",
       "2      21.01  3.50    Male     No  Sun  Dinner   3.0\n",
       "3      23.68  3.31    Male     No  Sun  Dinner   2.0\n",
       "4      24.59  3.61  Female     No  Sun  Dinner   4.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_excel('Tips.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(244, inplace=True)\n",
    "df['total_bill'] = df['total_bill'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 244 entries, 0 to 243\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   total_bill  244 non-null    float64\n",
      " 1   tip         244 non-null    float64\n",
      " 2   sex         244 non-null    object \n",
      " 3   smoker      244 non-null    object \n",
      " 4   day         244 non-null    object \n",
      " 5   time        244 non-null    object \n",
      " 6   size        244 non-null    float64\n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 15.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## La variable time y day , son categoricas por lo que convertiremos dichas variables en variables numericas para realizar la predicciòn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day_Sat</th>\n",
       "      <th>day_Sun</th>\n",
       "      <th>day_Thur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "   day_Sat  day_Sun  day_Thur\n",
       "0        0        1         0\n",
       "1        0        1         0\n",
       "2        0        1         0\n",
       "3        0        1         0\n",
       "4        0        1         0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_dummies = pd.get_dummies(df['day'], prefix= 'day', drop_first=True)\n",
    "day_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_Lunch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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      "text/plain": [
       "   time_Lunch\n",
       "0           0\n",
       "1           0\n",
       "2           0\n",
       "3           0\n",
       "4           0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_dummies = pd.get_dummies(df['time'], prefix= 'time', drop_first=True)\n",
    "time_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = df.merge(day_dummies, right_index=True, left_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>day_Sat</th>\n",
       "      <th>day_Sun</th>\n",
       "      <th>day_Thur</th>\n",
       "      <th>time_Lunch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size  day_Sat  day_Sun  \\\n",
       "0         16.99  1.01  Female     No   Sun  Dinner   2.0        0        1   \n",
       "1         10.34  1.66    Male     No   Sun  Dinner   3.0        0        1   \n",
       "2         21.01  3.50    Male     No   Sun  Dinner   3.0        0        1   \n",
       "3         23.68  3.31    Male     No   Sun  Dinner   2.0        0        1   \n",
       "4         24.59  3.61  Female     No   Sun  Dinner   4.0        0        1   \n",
       "..          ...   ...     ...    ...   ...     ...   ...      ...      ...   \n",
       "239       29.03  5.92    Male     No   Sat  Dinner   3.0        1        0   \n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner   2.0        1        0   \n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner   2.0        1        0   \n",
       "242       17.82  1.75    Male     No   Sat  Dinner   2.0        1        0   \n",
       "243       18.78  3.00  Female     No  Thur  Dinner   2.0        0        0   \n",
       "\n",
       "     day_Thur  time_Lunch  \n",
       "0           0           0  \n",
       "1           0           0  \n",
       "2           0           0  \n",
       "3           0           0  \n",
       "4           0           0  \n",
       "..        ...         ...  \n",
       "239         0           0  \n",
       "240         0           0  \n",
       "241         0           0  \n",
       "242         0           0  \n",
       "243         1           0  \n",
       "\n",
       "[244 rows x 11 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2 = df_2.merge(time_dummies, right_index=True, left_index=True)\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "      <th>day_Sat</th>\n",
       "      <th>day_Sun</th>\n",
       "      <th>day_Thur</th>\n",
       "      <th>time_Lunch</th>\n",
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       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
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       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
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       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip  size  day_Sat  day_Sun  day_Thur  time_Lunch\n",
       "0       16.99  1.01   2.0        0        1         0           0\n",
       "1       10.34  1.66   3.0        0        1         0           0\n",
       "2       21.01  3.50   3.0        0        1         0           0\n",
       "3       23.68  3.31   2.0        0        1         0           0\n",
       "4       24.59  3.61   4.0        0        1         0           0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.drop(['sex','smoker','day','time'], axis=1, inplace=True)\n",
    "df_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2['size'] = df_2.size.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 244 entries, 0 to 243\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   total_bill  244 non-null    float64\n",
      " 1   tip         244 non-null    float64\n",
      " 2   size        244 non-null    int32  \n",
      " 3   day_Sat     244 non-null    uint8  \n",
      " 4   day_Sun     244 non-null    uint8  \n",
      " 5   day_Thur    244 non-null    uint8  \n",
      " 6   time_Lunch  244 non-null    uint8  \n",
      "dtypes: float64(2), int32(1), uint8(4)\n",
      "memory usage: 17.6 KB\n"
     ]
    }
   ],
   "source": [
    "df_2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicciòn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_2.drop('tip', axis=1)\n",
    "y = df_2['tip']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initialize the estimators\n",
    "clf1 = DecisionTreeRegressor(random_state=42)\n",
    "clf2 = RandomForestRegressor(random_state=42)\n",
    "clf3 = BaggingRegressor(random_state=42)\n",
    "clf4 = GradientBoostingRegressor(random_state=42)\n",
    "clf5 = LinearRegression(normalize=True)\n",
    "clf6 = Ridge(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Precision MAE:  0.8548979591836735\n",
      "Test Precision MSE:  1.3138387755102041\n"
     ]
    }
   ],
   "source": [
    "clf1.fit(X_train, y_train)\n",
    "y_pred = clf1.predict(X_test)\n",
    "print(\"Test Precision MAE: \",mean_absolute_error(y_pred, y_test))\n",
    "print(\"Test Precision MSE: \",mean_squared_error(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Precision MAE:  0.7800982993197281\n",
      "Test Precision MSE:  0.9729517102097518\n"
     ]
    }
   ],
   "source": [
    "clf2.fit(X_train, y_train)\n",
    "y_pred = clf2.predict(X_test)\n",
    "print(\"Test Precision MAE: \",mean_absolute_error(y_pred, y_test))\n",
    "print(\"Test Precision MSE: \",mean_squared_error(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Precision MAE:  0.876469387755102\n",
      "Test Precision MSE:  1.215581530612245\n"
     ]
    }
   ],
   "source": [
    "clf3.fit(X_train, y_train)\n",
    "y_pred = clf3.predict(X_test)\n",
    "print(\"Test Precision MAE: \",mean_absolute_error(y_pred, y_test))\n",
    "print(\"Test Precision MSE: \",mean_squared_error(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Precision MAE:  0.7230449370784319\n",
      "Test Precision MSE:  0.7674252522761519\n"
     ]
    }
   ],
   "source": [
    "clf4.fit(X_train, y_train)\n",
    "y_pred = clf4.predict(X_test)\n",
    "print(\"Test Precision MAE: \",mean_absolute_error(y_pred, y_test))\n",
    "print(\"Test Precision MSE: \",mean_squared_error(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion el que mejor comportamiento arroja en sus resultados es GB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.26222219])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = [[24.50, 1, 0, 0, 1, 4]]\n",
    "clf4.predict(features)"
   ]
  }
 ],
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